code: def test_scoring():
      X, y = iris_data()
      clf1 = LogisticRegression(
          random_state=1, solver="liblinear", multi_class="ovr"
      )
      clf2 = DecisionTreeClassifier(random_state=1)
  
      X_train, X_test, y_train, y_test = train_test_split(
          X, y, test_size=0.25, random_state=123
      )
  
      score1 = clf1.fit(X_train, y_train).score(X_test, y_test)
      score2 = clf2.fit(X_train, y_train).score(X_test, y_test)
  
      assert round(score1, 2) == 0.97
      assert round(score2, 2) == 0.95
  
      f, p = combined_ftest_5x2cv(
          estimator1=clf1,
          estimator2=clf2,
          X=X,
          y=y,
          scoring="accuracy",
          random_seed=1,
      )
  
      assert round(f, 3) == 1.053, f
      assert round(p, 3) == 0.509, p
  
      f, p = combined_ftest_5x2cv(
          estimator1=clf1,
          estimator2=clf2,
          X=X,
          y=y,
          scoring="f1_macro",
          random_seed=1,
      )
  
      if Version(sklearn_version) < Version("0.20"):
          assert round(f, 3) == -1.510, f
          assert round(p, 3) == 0.191, p
      else:
          assert round(f, 3) == 1.046, f
          assert round(p, 3) == 0.513, p
  
file_path: mlxtend/mlxtend/evaluate/tests/test_combined_ftest_5x2cv.py
imports: from packaging.version import Version
  from sklearn import __version__
  from sklearn.linear_model import Lasso, LogisticRegression, Ridge
  from sklearn.model_selection import train_test_split
  from sklearn.tree import DecisionTreeClassifier
  from mlxtend.data import boston_housing_data, iris_data
  from mlxtend.evaluate import combined_ftest_5x2cv
label_desc: Use image node_id 2 for calling a global function with example usage: test_scoring() without return types